Evidence map›Paper›PMID 39501252›Full record

ArticleBMC medical informatics and decision making2024

Adaptive questionnaires for facilitating patient data entry in clinical decision support systems: methods and application to STOPP/START v2.

Jean-Baptiste Lamy, Abdelmalek Mouazer, Romain Léguillon, Romain Lelong, Stéfan Darmoni, Karima Sedki, Sophie Dubois, Hector Falcoff

Erratum issuedAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Jean-Baptiste LamyINSERM, Sorbonne Universite, Universite Sorbonne Paris Nord, Laboratory of Medical Informatics and Knowledge Engineering in e-Health, LIMICS, 15 rue de l'école de médecine, Paris, 75006, France. jean-baptiste.lamy@inserm.fr.
Abdelmalek MouazerINSERM, Sorbonne Universite, Universite Sorbonne Paris Nord, Laboratory of Medical Informatics and Knowledge Engineering in e-Health, LIMICS, 15 rue de l'école de médecine, Paris, 75006, France.
Romain LéguillonINSERM, Sorbonne Universite, Universite Sorbonne Paris Nord, Laboratory of Medical Informatics and Knowledge Engineering in e-Health, LIMICS, 15 rue de l'école de médecine, Paris, 75006, France.
Romain LelongINSERM, Sorbonne Universite, Universite Sorbonne Paris Nord, Laboratory of Medical Informatics and Knowledge Engineering in e-Health, LIMICS, 15 rue de l'école de médecine, Paris, 75006, France.
Stéfan DarmoniINSERM, Sorbonne Universite, Universite Sorbonne Paris Nord, Laboratory of Medical Informatics and Knowledge Engineering in e-Health, LIMICS, 15 rue de l'école de médecine, Paris, 75006, France.
Karima SedkiINSERM, Sorbonne Universite, Universite Sorbonne Paris Nord, Laboratory of Medical Informatics and Knowledge Engineering in e-Health, LIMICS, 15 rue de l'école de médecine, Paris, 75006, France.
Sophie DuboisSFTG Recherche (Société de Formation Thérapeutique du Généraliste), Paris, 75013, France.
Hector FalcoffSFTG Recherche (Société de Formation Thérapeutique du Généraliste), Paris, 75013, France.

Funding

Agence Nationale de la Recherche,France ANR-20-CE19-0017
6 · The paper itself

Abstract

Clinical decision support systems are software tools that help clinicians to make medical decisions. However, their acceptance by clinicians is usually rather low. A known problem is that they often require clinicians to manually enter a lot of patient data, which is long and tedious. Existing solutions, such as the automatic data extraction from electronic health record, are not fully satisfying, because of low data quality and availability. In practice, many systems still include long questionnaire for data entry. In this paper, we propose an original solution to simplify patient data entry, using an adaptive questionnaire, i.e. a questionnaire that evolves during user interaction, showing or hiding questions dynamically. Considering a rule-based decision support systems, we designed methods for determining the relationships between rules and translating the system's clinical rules into display rules that determine the items to show in the questionnaire, and methods for determining the optimal order of priority among the items in the questionnaire. We applied this approach to a decision support system implementing STOPP/START v2, a guideline for managing polypharmacy. We show that it permits reducing by about two thirds the number of clinical conditions displayed in the questionnaire, both on clinical cases and real patient data. Presented to clinicians during focus group sessions, the adaptive questionnaire was found "pretty easy to use". In the future, this approach could be applied to other guidelines, and adapted for data entry by patients.

Indexed as

Decision Support Systems, ClinicalElectronic Health RecordsHumansPolypharmacySurveys and QuestionnairesAdaptive questionnaireClinical decision support systemsMedication reviewPatient data entryPolypharmacy managementSTOPP/START v2

Identifiers

PMID39501252
PMCPMC11539734

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.